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Introducing Regular Expressions

If you’re a programmer new to regular expressions, this easy-to-follow guide is a great place to start. You’ll learn the fundamentals step-by-step with the help of numerous examples, discovering first-hand how to match, extract, and transform text by matching specific words, characters, and patterns. Regular expressions are an essential part of a programmer’s toolkit, available in various Unix utlilities as well as programming languages such as Perl, Java, JavaScript, and C#. When you’ve finished this book, you’ll be familiar with the most commonly used syntax in regular expressions, and you’ll understand how using them will save you considerable time. Discover what regular expressions are and how they work Learn many of the differences between regular expressions used with command-line tools and in various programming languages Apply simple methods for finding patterns in text, including digits, letters, Unicode characters, and string literals Learn how to use zero-width assertions and lookarounds Work with groups, backreferences, character classes, and quantifiers Use regular expressions to mark up plain text with HTML5

The Conservative Papers

Data Algorithms

If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You’ll learn how to implement the appropriate MapReduce solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactions Data mining algorithms (K-means, KNN, and Naive Bayes) Using huge genomic data to sequence DNA and RNA Naive Bayes theorem and Markov chains for data and market prediction Recommendation algorithms and pairwise document similarity Linear regression, Cox regression, and Pearson correlation Allelic frequency and mining DNA Social network analysis (recommendation systems, counting triangles, sentiment analysis)